Because of the enhancement of image processing performance of image processor, video devices (e.g. IP cameras) installed in dwellings, workplaces or vehicles have been able to simultaneously produce multiple video streams under various resolutions. For example, the video device provides a mobile device with a video stream under a lower resolution as providing a surveillance system with a video stream under a higher resolution. For example, if the video device provides a surveillance system with a video stream under a high resolution, the surveillance system will store video frames of the video stream of the high resolution in a storage media. Therefore, if a certain dispute event or serious accident occurs one day, it needs a clear image for a computing device (e.g. a central computing unit (CPU)) to perform image analysis to recognize the image of a target and clarify the real situation or identify a certain person.
On the other hand, more and more computer vision technologies are applied to real-time or later image analysis, so as to mitigate users' loading. However, when intending to recognize an object appearing in video frames under a high resolution, a computing device needs to spend more time in processing these video frames in order to efficiently recognize the object. For example, processing video frames includes decompressing and then analyzing them. The higher the resolution of a video stream is, the heavier the computation for the decompression and image analysis of video frames is. Therefore, if a surveillance system that multiple IP cameras link to through networks, intends to analyze and recognize an object in a great deal of high-resolution images, a computing device in the surveillance system will be subjected to more heavy load.